1. Field of the Invention
The present invention is related to a method for tracking the progression of damage and predicting the remaining useful life of machinery, and in particular pertains to tracking and predicting evolving xe2x80x9cslowxe2x80x9d damage using only readily available, directly observable xe2x80x9cfastxe2x80x9d quantities in systems having time scale separation.
2. Description of the Related Art
To stay competitive in modern, rapidly developing economies, industry strives to wholly utilize the useful life of their products and machinery without sacrificing environmental, personnel or consumer safety. The development of condition based maintenance and failure prediction technology addresses the important efficiency and safety issue aspects of using machinery to the fullest extent of its useful life. A problem exists in that most processes responsible for system (i.e., machine) failures are hidden from an observer. That is, the processes responsible for system failures are not directly observable. In many cases, the damage physics and/or an adequate mathematical representation of the system failure processes are not known. Even when the damage physics are known, it is usually difficult, if not impossible, to obtain a direct measure of the damage state of a particular machine without removing machinery from operation, and thus losing productivity.
As a matter of clarity, two of the main steps in the machinery condition monitoring problem are damage diagnosis and damage prognosis. In general, diagnosis encompasses damage detection, localization, and assessment. The step of damage prognosis includes the prediction of when the system will completely fail or the damage state of the system will reach a predetermined failure value. Prognosis is contingent on successful damage assessment. That is, given a successful estimate of the current damage state, and given a suitable damage evolution law, or means of determining such a law, a prediction of when the system will fail or when the damage state has reach a predetermined failure value is obtainable.
Most currently available condition monitoring technology focuses on diagnosis, particularly, damage detection. Several strategies exist for addressing the problem of damage detection. One approach is data based, or heuristic wherein one looks for changes due to the damage accumulation in time and/or frequency domain statistics of various types. For nonlinear systems exhibiting chaotic responses, it is customary to use estimates of long-time chaotic invariant measures, such as the correlation dimension or Lyapunov exponents. Other advanced techniques use expert systems or fuzzy logic and genetic algorithms. A feature of such methods is the simplicity with which they may be implemented, and that they can work very well at times. Most heuristic methods however serve as purely damage detection methods, that is, no damage state assessment is provided.
Even when the severity of damage can be estimated, it is usually difficult to establish a direct one-to-one connection between the damage state and the change in the heuristic statistic or feature vector There is no general theoretical basis for predicting a priori, without the benefit of a good model or experiment, whether any given feature vector will provide an adequate indication of the damage state for a particular system.
Another approach is model-based. The model-based approach addresses some of the shortcomings of the purely statistical approach discussed above, typically at the expense of more difficult development and implementation. In cases where an analytical (i.e., mathematical) model is available for the system under consideration, it may be possible to establish a functional connection between the drifting parameters and a particular feature vector. However, due to the general difficulty of developing physics-based mathematical models for many systems, such analytical models are not usually available.
The lack of an analytical model has been addressed by developing data-based models. The vast majority of model-based approaches have focused on linear systems. For linear systems, tools such as autoregressive models and/or frequency response functions have been used for fault detection in, for example, ball bearings.
Nonlinear systems on the other hand have been analyzed using neural networks. More recently, approaches have been developed that attempt to monitor damage using the asymptotic invariants of chaotic attractors. For example, estimates of the correlation dimension and Lyapunov exponents have been used to detect changes in the xe2x80x9cchaotic signaturexe2x80x9d of the vibration signals from a damaged gearbox. However, while it is clear that such asymptotic invariants can be used to detect changes in a system caused by damage, they are unsuitable for actual damage tracking, since they do not vary monotonically with the hidden variable. This is a consequence of the fact that the asymptotic invariants do not vary smoothly with system parameters as the system passes through various bifurcations during the experiments. Futhermore, such methods depend on the existence of a chaotic response in the system being monitored.
Additional approaches have been based on various hybrids of methods including those discussed above, and others. In many cases, extensive attention has been given to the use of specific physical properties of structures for damage detection and identification, such as, most notably, mode shapes. A disadvantage of many of such methods for damage diagnostics, however, is that they are highly application dependent.
The failure prognostics problem, by itself, is still in the developmental stages. Currently available prognostic methods can be divided into methods based on deterministic and probabilistic or stochastic modeling of fault or damage propagation. Such methods are still application dependent, since they are closely tied to a particular damage detection problem. In addition, these methods cannot be considered general or comprehensive solutions, since their applicability is contingent upon successful damage state assessment that is provided by some suitable damage detection method.
It is therefore an object of the present teachings to provide a method of damage diagnosis and prognosis that addresses the foregoing and other problems and limitations of available technologies.
The present invention pertains to a general purpose machinery diagnostic and prognostic method and apparatus for tracking and predicting evolving damage using only available xe2x80x9cmacroscopicxe2x80x9d observable quantities. The damage is considered as occurring in a hierarchical dynamical system including a directly observable, xe2x80x9cfastxe2x80x9d subsystem that is coupled with a hidden, xe2x80x9cslowxe2x80x9d subsystem describing damage evolution. In one aspect thereof, the method provides damage diagnostics in the form of continuous xe2x80x9cgray-scalexe2x80x9d estimates of current machine health (or, equivalently, damage state), and failure prognostics accomplished using measurements from the fast subsystem and a model of the slow subsystem.
Damage tracking is accomplished using a two-time-scale modeling strategy based on appropriate state-space modeling of the fast subsystem. In particular, short-time predictive models are constructed using data obtained from the reference (e.g., undamaged) fast subsystem. Fast-time data for the damaged system is then collected at future times, and used to estimate the short-time reference model prediction error (i.e., STIRMOP). The STIRMOP over a given data record is used to generate a tracking metric, or measure, of the current damage state using a linear recursive estimator. Recursive, nonlinear filtering is then used on the estimated damage state to estimate the remaining useful life of the machinery component.
Due to its general formulation, the present invention is applicable to a wide variety of systems possessing drastically different damage physics. For example, the method can be applied to vibrating structural systems (such as, for example, beams and trusses) with cracks that grow to complete failure; gears with failing teeth; electromechanical systems with failing batteries or other power sources; electrical systems with failing or degrading discrete components such as resistors or capacitors; or precision machinery that is gradually drifting out of alignment.
The present invention is capable of providing accurate predictions of remaining useful life, in real-time, beginning in advance of the final, complete failure of the system. The present method generates a feature vector of a particular and novel type, the short time reference model prediction error, or STIRMOP. Unlike arbitrary feature vectors used in other conditioning monitoring approaches, the evolution of the STIRMOP in time can be directly and unambiguously related to changes in the hidden damage state of the system. Thus, using the STIRMOP feature vector, it is possible to give a continuous (i.e., real time) presentation of the current state of damage in the system. The present method does not merely indicate that a machine is no longer operating normally, but provides xe2x80x9cgray-scalexe2x80x9d damage information, indicating the current degree of damage compared to the initial state of the machine. In other words, using STIRMOP it is possible to track the evolution of damage in the system.